Vehicle Detection Project

The goals / steps of this project are the following:

• Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
• Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
• Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
• Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
• Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
• Estimate a bounding box for vehicles detected.

Histogram of Oriented Gradients (HOG)

1. Explain how (and identify where in your code) you extracted HOG features from the training images.

The code for this step is contained in lines 131 through 143 of the file called helper_functions.py.

I started by reading in all the vehicle and non-vehicle images.

Then I randomly picked an image from each classes and experienced with them. I explored HOG feature extraction on the same image with different skimage.hog() parameters (orientations, pixels_per_cell, and cells_per_block).

Parameters name	First set	Second set	Third set
Orientation	9	9	9
Pixels per cell	8	4	16
Cells per block	2	2	2
Visualize	True	True	True
Feature vector	False	False	False

Then I tried spatial binning and color histogram in different color spaces

Parameters name	First set	Second set	Third set	Fourth set
Color spaces	'RGb'	'RGB'	'HLS'	'YCrCb'
Spatial size	(32, 32)	(32, 32)	(32, 32)	(32, 32)
Histogram bins	32	32	32	32
Histogram range	(0, 256)	(0, 256)	(0, 256)	(0, 256)

Import libraries

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import random

% matplotlib inline

Import functions

from helper_functions import data_look

Initialization

# Images are in PNG format and divided into vehichles and non-vehicles
vehicles_images = glob.glob('./training_dataset/vehicles/*/*.png')
non_vehicles_images = glob.glob('./training_dataset/non-vehicles/*/*.png')

vehicles_all = []
non_vehicles_all = []

Reading all images

for image in vehicles_images:
    vehicles_all.append(image)

for image in non_vehicles_images:
    non_vehicles_all.append(image)

# Shuffle image data
random.shuffle(vehicles_all)
random.shuffle(non_vehicles_all)

# Take the first 100 images for testing
vehicles_test = vehicles_all[:1000]
non_vehicles_test = non_vehicles_all[:1000]

# Store some characteristics of the dataset
data_info_test = data_look(vehicles_test, non_vehicles_test)

print('Your function returned a count of', 
      data_info_test["n_vehicles"], ' vehicles and', 
      data_info_test["n_non_vehicles"], ' non-vehicles')
print('of size: ',data_info_test["image_shape"], ' and data type:', 
      data_info_test["data_type"])

Your function returned a count of 1000  vehicles and 1000  non-vehicles
of size:  (64, 64, 3)  and data type: float32

Plot one sample image from each classes

# Return a random index within range of the minimum length in both lists
idx_max = np.minimum(data_info_test["n_vehicles"], data_info_test["n_non_vehicles"])
idx_random = np.random.randint(0, idx_max)
print("Current random index is", idx_random)

# Read in vehicles / non-vehicles images
vehicle_image_test = mpimg.imread(vehicles_test[idx_random])
non_vehicle_image_test = mpimg.imread(non_vehicles_test[idx_random])

# Plot the examples
f, axs = plt.subplots(1, 2, figsize=(30, 15))
axs = axs.ravel()

axs[0].imshow(vehicle_image_test)
axs[0].set_title('Example Vehicle Image', fontsize = 20)

axs[1].imshow(non_vehicle_image_test)
axs[1].set_title('Example Non-vehicle Image', fontsize = 20)

f.tight_layout()
plt.subplots_adjust(left=0., right=0.4, top=0.9, bottom=0.)

Current random index is 236

Apply HOG with different parameters

Import functions

from helper_functions import get_hog_features

Define parameters

# Define HOG parameters
# First set
orient = 9
pix_per_cell = 8
cell_per_block = 2
vis = True
feature_vec = False

# Second set
orient_2 = 9
pix_per_cell_2 = 4
cell_per_block_2 = 2
vis_2 = True
feature_vec_2 = False

# Third set
orient_3 = 9
pix_per_cell_3 = 16
cell_per_block_3 = 2
vis_3 = True
feature_vec_3 = False

Covert to grayscale

# Convert to grayscale
gray_vehicle_test = cv2.cvtColor(vehicle_image_test, cv2.COLOR_RGB2GRAY)
gray_non_vehicle_test = cv2.cvtColor(non_vehicle_image_test, cv2.COLOR_RGB2GRAY)

Return HOG features and visualization

# Return HOG features and visualization
# First set
features_vehicle, hog_image_vehicle = get_hog_features(gray_vehicle_test, orient,
                                                      pix_per_cell, cell_per_block,
                                                      vis, feature_vec)

features_non_vehicle, hog_image_non_vehicle = get_hog_features(gray_non_vehicle_test, orient,
                                                      pix_per_cell, cell_per_block,
                                                      vis, feature_vec)

# Second set
features_vehicle_2, hog_image_vehicle_2 = get_hog_features(gray_vehicle_test, orient_2,
                                                      pix_per_cell_2, cell_per_block_2,
                                                      vis_2, feature_vec_2)

features_non_vehicle_2, hog_image_non_vehicle_2 = get_hog_features(gray_non_vehicle_test, orient_2,
                                                      pix_per_cell_2, cell_per_block_2,
                                                      vis_2, feature_vec_2)

# Third set
features_vehicle_3, hog_image_vehicle_3 = get_hog_features(gray_vehicle_test, orient_3,
                                                      pix_per_cell_3, cell_per_block_3,
                                                      vis_3, feature_vec_3)

features_non_vehicle_3, hog_image_non_vehicle_3 = get_hog_features(gray_non_vehicle_test, orient_3,
                                                      pix_per_cell_3, cell_per_block_3,
                                                      vis_3, feature_vec_3)

Plot images

# Plot the examples
f, axs = plt.subplots(2, 4, figsize=(30, 30))
axs = axs.ravel()

axs[0].imshow(gray_vehicle_test)
axs[0].set_title('Grayscale Vehicle Image', fontsize = 20)

axs[1].imshow(hog_image_vehicle)
axs[1].set_title('HOG Visualization Vehicle Image', fontsize = 20)

axs[2].imshow(hog_image_vehicle_2)
axs[2].set_title('HOG Visualization Vehicle Image 2', fontsize = 20)

axs[3].imshow(hog_image_vehicle_3)
axs[3].set_title('HOG Visualization Vehicle Image 3', fontsize = 20)

axs[4].imshow(gray_non_vehicle_test)
axs[4].set_title('Grayscale Non-Vehicle Image', fontsize = 20)

axs[5].imshow(hog_image_non_vehicle)
axs[5].set_title('HOG Visualization Non-vehicle Image', fontsize = 20)

axs[6].imshow(hog_image_non_vehicle_2)
axs[6].set_title('HOG Visualization Non-vehicle Image 2', fontsize = 20)

axs[7].imshow(hog_image_non_vehicle_3)
axs[7].set_title('HOG Visualization Non-vehicle Image 3', fontsize = 20)

f.tight_layout()
plt.subplots_adjust(left=0.1, right=0.85, top=0.85, bottom=0.3)

Apply spatial binning and color histogram in different color spaces

Import libraries

from sklearn.preprocessing import StandardScaler
from mpl_toolkits.mplot3d import Axes3D

Import functions

from helper_functions import extract_features_bin_spatial_hist, plot3d

Define parameters

# RGB
color_space = 'RGB'
spatial_size=(32, 32)
hist_bins=32
hist_range=(0, 256)

# HSV
color_space_2 = 'RGB'
spatial_size_2 = (32, 32)
hist_bins_2 = 32
hist_range_2 = (0, 256)

# HLS
color_space_3 = 'HLS'
spatial_size_3 = (32, 32)
hist_bins_3 = 32
hist_range_3 = (0, 256)

# YCrCb
color_space_4 = 'YCrCb'
spatial_size_4 = (32, 32)
hist_bins_4 = 32
hist_range_4 = (0, 256)

Return spatial binning of color features

# RGB
features_bin_spatial_hist_vehicle = extract_features_bin_spatial_hist(vehicles_test, color_space, spatial_size,
                        hist_bins, hist_range)

features_bin_spatial_hist_non_vehicle = extract_features_bin_spatial_hist(non_vehicles_test, color_space, spatial_size,
                        hist_bins, hist_range)

# HSV
features_bin_spatial_hist_vehicle_2 = extract_features_bin_spatial_hist(vehicles_test, color_space_2, spatial_size_2,
                        hist_bins_2, hist_range_2)

features_bin_spatial_hist_non_vehicle_2 = extract_features_bin_spatial_hist(non_vehicles_test, color_space_2, spatial_size_2,
                        hist_bins_2, hist_range_2)

# HLS
features_bin_spatial_hist_vehicle_3 = extract_features_bin_spatial_hist(vehicles_test, color_space_3, spatial_size_3,
                        hist_bins_3, hist_range_3)

features_bin_spatial_hist_non_vehicle_3 = extract_features_bin_spatial_hist(non_vehicles_test, color_space_3, spatial_size_3,
                        hist_bins_3, hist_range_3)

# YCrCb
features_bin_spatial_hist_vehicle_4 = extract_features_bin_spatial_hist(vehicles_test, color_space_4, spatial_size_4,
                        hist_bins_4, hist_range_4)

features_bin_spatial_hist_non_vehicle_4 = extract_features_bin_spatial_hist(non_vehicles_test, color_space_4, spatial_size_4,
                        hist_bins_4, hist_range_4)

Normalize feature vectors

# RGB
if len(features_bin_spatial_hist_vehicle) > 0:
    # Create an array stack of feature vectors
    X = np.vstack((features_bin_spatial_hist_vehicle, features_bin_spatial_hist_non_vehicle)).astype(np.float64)
    # Fit a per-column scaler
    X_scaler = StandardScaler().fit(X)
    # Apply the scaler to X
    scaled_X = X_scaler.transform(X)
else: 
    print('Your function only returns empty feature vectors for RGB')
    
# HSV
if len(features_bin_spatial_hist_vehicle_2) > 0:
    # Create an array stack of feature vectors
    X_2 = np.vstack((features_bin_spatial_hist_vehicle_2, features_bin_spatial_hist_non_vehicle_2)).astype(np.float64)
    # Fit a per-column scaler
    X_scaler_2 = StandardScaler().fit(X_2)
    # Apply the scaler to X
    scaled_X_2 = X_scaler_2.transform(X_2)
else: 
    print('Your function only returns empty feature vectors for HSV')
    
# HLS
if len(features_bin_spatial_hist_vehicle_3) > 0:
    # Create an array stack of feature vectors
    X_3 = np.vstack((features_bin_spatial_hist_vehicle_3, features_bin_spatial_hist_non_vehicle_3)).astype(np.float64)
    # Fit a per-column scaler
    X_scaler_3 = StandardScaler().fit(X_3)
    # Apply the scaler to X
    scaled_X_3 = X_scaler_3.transform(X_3)
else: 
    print('Your function only returns empty feature vectors for HLS')
    
# YCrCb
if len(features_bin_spatial_hist_vehicle_4) > 0:
    # Create an array stack of feature vectors
    X_4 = np.vstack((features_bin_spatial_hist_vehicle_4, features_bin_spatial_hist_non_vehicle_4)).astype(np.float64)
    # Fit a per-column scaler
    X_scaler_4 = StandardScaler().fit(X_4)
    # Apply the scaler to X
    scaled_X_4 = X_scaler_4.transform(X_4)
else: 
    print('Your function only returns empty feature vectors for YCrCb')

Plot images

# Plot image

fig = plt.figure(figsize=(12,4))

# RGB
plt.subplot(241)
plt.plot(X[idx_random])
plt.title('Raw Features(RGB)')
plt.subplot(245)
plt.plot(scaled_X[idx_random])
plt.title('Normalized Features(RGB)')

# HSV
plt.subplot(242)
plt.plot(X_2[idx_random])
plt.title('Raw Features(HSV)')
plt.subplot(246)
plt.plot(scaled_X_2[idx_random])
plt.title('Normalized Features(HSV)')

# HLS
plt.subplot(243)
plt.plot(X_3[idx_random])
plt.title('Raw Features(HLS)')
plt.subplot(247)
plt.plot(scaled_X_3[idx_random])
plt.title('Normalized Features(HLS)')

# YCrCb
plt.subplot(244)
plt.plot(X_4[idx_random])
plt.title('Raw Features(YCrCb)')
plt.subplot(248)
plt.plot(scaled_X_4[idx_random])
plt.title('Normalized Features(YCrCb)')

fig.tight_layout()

Plot image in 3D

# Read a color image
img = cv2.imread(vehicles_test[idx_random])
#img = cv2.imread("000275.png")
#img = mpimg.imread(vehicles[idx_random])

# Select a small fraction of pixels to plot by subsampling it
scale = max(img.shape[0], img.shape[1], 64) / 64  # at most 64 rows and columns
img_small = cv2.resize(img, (np.int(img.shape[1] / scale), np.int(img.shape[0] / scale)), interpolation=cv2.INTER_NEAREST)

# Convert subsampled image to desired color space(s)
# RGB
img_small_RGB = cv2.cvtColor(img_small, cv2.COLOR_BGR2RGB)  # OpenCV uses BGR, matplotlib likes RGB
img_small_rgb = img_small_RGB / 255.  # scaled to [0, 1], only for plotting
# HSV
img_small_HSV = cv2.cvtColor(img_small, cv2.COLOR_BGR2HSV)
# HLS
img_small_HLS = cv2.cvtColor(img_small, cv2.COLOR_BGR2HLS)
# YCrCb
img_small_YCrCb = cv2.cvtColor(img_small, cv2.COLOR_BGR2YCrCb)


# Plot and show
plot3d(img_small_RGB, img_small_rgb)
plt.show()

plot3d(img_small_HSV, img_small_rgb, axis_labels=list("HSV"))
plt.show()

plot3d(img_small_HLS, img_small_rgb, axis_labels=list("HLS"))
plt.show()

plot3d(img_small_YCrCb, img_small_rgb, axis_labels=list("YCrCb"))
plt.show()

2. Explain how you settled on your final choice of HOG parameters.

I tried various combinations of parameters and came up th following decision with explanation. The documentation for hog() function can be found here.

• For orientations, either 9 or 17 would be acceptable. I go with orientation = 9.
• For pixels_per_cell, smaller number will return more cells. For a 64 by 64 pixels picture, pix per cell = 8 is good enough.
• For cells per block, either 2 or 4 would be good. I go with cell_per_block = 2.
• For visualize, I don't need the image return back in the following cells. Set False to it.
• For feature_vector, I need it to be a 1 demensional array for concatenating to other features. Therefore, set True to it.
• For multichannel, I am using HLS color space with ALL channels

---

Final list
orient_final = 9
pix_per_cell_final = 8
cell_per_block_final = 2
vis_final = False
feature_vec_final = True
multichannel_final = 'ALL'

3. Describe how (and identify where in your code) you trained a classifier using your selected HOG features (and color features if you used them).

I trained a linear SVM using the following features:

• Color features(spatial binning, color histogram)
  color_space_final = 'HLS'
  spatial_size_final = (32, 32)
  hist_bins_final = 32
  hist_range_final = (0, 256)

• HOG features
  orient_final = 9
  pix_per_cell_final = 8
  cell_per_block_final = 2
  vis_final = False
  feature_vec_final = True
  multichannel_final = 'ALL'

---

Here are the training steps:

• Extract the specific features from the dataset
• Normalie the extracted features vector
• Define labels vector
• Split up and randomize on both extracted features vector and labels vector
  • Return training dataset and test dataset
• Create SVM
• Fit the training dataset into SVM
• Evaluate the accuracy of SVM
• Predict the result with the test dataset
• Done

Import libraries

import time
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.cross_validation import train_test_split
# But, if you are using scikit-learn >= 0.18 then use this:
# from sklearn.model_selection import train_test_split

/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

Import functions

from helper_functions import extract_features_single_image

Define parameters

# Define parameters
color_space_final = 'HLS'  
spatial_size_final = (32, 32)  
hist_bins_final = 32  
hist_range_final = (0, 256)  

orient_final = 9  
pix_per_cell_final = 8  
cell_per_block_final = 2  
vis_final = False # Hardcoded in the function extract_features_single_image()
feature_vec_final = True # Hardcoded in the function extract_features_single_image()
multichannel_final = "ALL"   # Can be 0, 1, 2, or "ALL"
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off

Extract the specific features from the dataset

t1=time.time()

# Create a list to append feature vectors to
vehicle_features_all = []
non_vehicle_features_all = []

# Extract features from each images
for vehicle_img_path in vehicles_all:
    # Read in vehicles
    vehicle_image = mpimg.imread(vehicle_img_path)
    # Extract features from image
    single_image_features = extract_features_single_image(vehicle_image, color_space=color_space_final, spatial_size=spatial_size_final, 
                                                          hist_bins=hist_bins_final, hist_range=hist_range_final, orient=orient_final, 
                                                          pix_per_cell=pix_per_cell_final, cell_per_block=cell_per_block_final, hog_channel=multichannel_final, 
                                                          spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
    # Append the new feature vector to the features list
    vehicle_features_all.append(single_image_features)

t2 = time.time()
print(round(t2-t1, 2), 'Seconds to extract features from vehicle images.')
    
# Extract features from each images
for non_vehicle_img_path in non_vehicles_all:
    # Read in vehicles
    non_vehicle_image = mpimg.imread(non_vehicle_img_path)
    # Extract features from image
    single_image_features = extract_features_single_image(non_vehicle_image, color_space=color_space_final, spatial_size=spatial_size_final, 
                                                          hist_bins=hist_bins_final, hist_range=hist_range_final, orient=orient_final, 
                                                          pix_per_cell=pix_per_cell_final, cell_per_block=cell_per_block_final, hog_channel=multichannel_final, 
                                                          spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
    # Append the new feature vector to the features list
    non_vehicle_features_all.append(single_image_features)

t3 = time.time()
print(round(t3-t2, 2), 'Seconds to extract features from non-vehicle images.')

115.47 Seconds to extract features from vehicle images.
117.92 Seconds to extract features from non-vehicle images.

Normalie the extracted features vector

# Normalize
if len(vehicle_features_all) > 0:
    # Create an array stack of feature vectors
    X_final = np.vstack((vehicle_features_all, non_vehicle_features_all)).astype(np.float64)                        
    # Fit a per-column scaler
    X_scaler_final = StandardScaler().fit(X_final)
    # Apply the scaler to X
    scaled_X_final = X_scaler_final.transform(X_final)
else: 
    print('Your function only returns empty feature vectors for RGB')

Define labels vector

# Define the labels vector
y_final = np.hstack((np.ones(len(vehicle_features_all)), 
               np.zeros(len(non_vehicle_features_all))))

Split up and randomize on both extracted features vector and labels vector

# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
    scaled_X_final, y_final, test_size=0.2, random_state=rand_state)

print('Using spatial binning of:',spatial_size_final,
    'and', hist_bins_final,'histogram bins')
print('Feature vector length:', len(X_train[0]))

Using spatial binning of: (32, 32) and 32 histogram bins
Feature vector length: 8460

Create SVM

# Use a linear SVC 
svc = LinearSVC()

Fit the training dataset into SVM

# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')

37.35 Seconds to train SVC...

Evaluate the accuracy of SVM

# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))

Test Accuracy of SVC =  0.9786

Predict the result with the test dataset

# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')

My SVC predicts:  [ 1.  0.  0.  0.  1.  0.  1.  0.  1.  1.]
For these 10 labels:  [ 1.  0.  0.  0.  1.  0.  1.  0.  0.  1.]
0.00356 Seconds to predict 10 labels with SVC

Sliding Window Search

1. Describe how (and identify where in your code) you implemented a sliding window search. How did you decide what scales to search and how much to overlap windows?

I decided to search window at different scale at (64, 64) (96, 96) and overlap at (0.5, 0.5) based on the experiment below. The code below is used to evaluate the performance of different combination of window scale and overlap value.
From the final result, I come up the following conclusions:

• Larger overlap will result in more match windows
• Less overlap will result in less match window, nevertheless, not good for the following steps "add heat" and "filter".
• Smaller window will give you more match windows to analyze
• Bigger window will result less overlap, not good for the following steps "add heat" and "filter".

I explored sliding window search on the same image with different parameteres.

Parameters name	First set	Second set	Third set	Fourth set	Fifth set
Window size	(64, 64)	(64, 64)	(64, 64)	(32, 32)	(96, 96)
Window overlap	(0.5, 0.5)	(0.2, 0.2)	(0.8, 0.8)	(0.5, 0.5)	(0.5, 0.5)

Import functions

from helper_functions import slide_window, search_windows, draw_boxes

Read in test image

image = mpimg.imread('./test_images/test1.jpg')
draw_image = np.copy(image)

# Uncomment the following line if you extracted training
# data from .png images (scaled 0 to 1 by mpimg) and the
# image you are searching is a .jpg (scaled 0 to 255)
image = image.astype(np.float32)/255

Define Parameters

y_max = image.shape[0]
y_start_stop = [np.int(y_max/2), y_max] # Min and max in y to search in slide_window()

# First set
xy_window_1 = (64, 64)
xy_overlap_1 = (0.5, 0.5)

# Second set
xy_window_2 = (64, 64)
xy_overlap_2 = (0.2, 0.2)

# Third set
xy_window_3 = (64, 64)
xy_overlap_3 = (0.8, 0.8)

# Fourth set
xy_window_4 = (32, 32)
xy_overlap_4 = (0.5, 0.5)

# Fifth set
xy_window_5 = (96, 96)
xy_overlap_5 = (0.5, 0.5)

Search windows

# First set
windows_1 = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                    xy_window=xy_window_1, xy_overlap=xy_overlap_1)

hot_windows_1 = search_windows(image, windows_1, svc, X_scaler_final, color_space=color_space_final, 
                        spatial_size=spatial_size_final, hist_bins=hist_bins_final, hist_range = hist_range_final, 
                        orient=orient_final, pix_per_cell=pix_per_cell_final, 
                        cell_per_block=cell_per_block_final, 
                        hog_channel=multichannel_final, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)                       

window_img_1 = draw_boxes(draw_image, hot_windows_1, color=(0, 0, 255), thick=6)

# Second set
windows_2 = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                    xy_window=xy_window_2, xy_overlap=xy_overlap_2)

hot_windows_2 = search_windows(image, windows_2, svc, X_scaler_final, color_space=color_space_final, 
                        spatial_size=spatial_size_final, hist_bins=hist_bins_final, hist_range = hist_range_final, 
                        orient=orient_final, pix_per_cell=pix_per_cell_final, 
                        cell_per_block=cell_per_block_final, 
                        hog_channel=multichannel_final, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)                       

window_img_2 = draw_boxes(draw_image, hot_windows_2, color=(0, 0, 255), thick=6)

# Third set
windows_3 = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                    xy_window=xy_window_3, xy_overlap=xy_overlap_3)

hot_windows_3 = search_windows(image, windows_3, svc, X_scaler_final, color_space=color_space_final, 
                        spatial_size=spatial_size_final, hist_bins=hist_bins_final, hist_range = hist_range_final, 
                        orient=orient_final, pix_per_cell=pix_per_cell_final, 
                        cell_per_block=cell_per_block_final, 
                        hog_channel=multichannel_final, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)                       

window_img_3 = draw_boxes(draw_image, hot_windows_3, color=(0, 0, 255), thick=6)

# Fourth set
windows_4 = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                    xy_window=xy_window_4, xy_overlap=xy_overlap_4)

hot_windows_4 = search_windows(image, windows_4, svc, X_scaler_final, color_space=color_space_final, 
                        spatial_size=spatial_size_final, hist_bins=hist_bins_final, hist_range = hist_range_final, 
                        orient=orient_final, pix_per_cell=pix_per_cell_final, 
                        cell_per_block=cell_per_block_final, 
                        hog_channel=multichannel_final, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)                       

window_img_4 = draw_boxes(draw_image, hot_windows_4, color=(0, 0, 255), thick=6)

# Fifth set
windows_5 = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                    xy_window=xy_window_5, xy_overlap=xy_overlap_5)

hot_windows_5 = search_windows(image, windows_5, svc, X_scaler_final, color_space=color_space_final, 
                        spatial_size=spatial_size_final, hist_bins=hist_bins_final, hist_range = hist_range_final, 
                        orient=orient_final, pix_per_cell=pix_per_cell_final, 
                        cell_per_block=cell_per_block_final, 
                        hog_channel=multichannel_final, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)                       

window_img_5 = draw_boxes(draw_image, hot_windows_5, color=(0, 0, 255), thick=6)

Plot images

# Plot the examples
f, axs = plt.subplots(2, 3, figsize=(36, 24))
axs = axs.ravel()

axs[0].imshow(window_img_1)
axs[0].set_title('xy_window=(64,64) xy_overlap=(0.5,0.5)', fontsize = 20)

axs[1].imshow(window_img_2)
axs[1].set_title('xy_window=(64,64) xy_overlap=(0.2,0.2)', fontsize = 20)

axs[2].imshow(window_img_3)
axs[2].set_title('xy_window=(64,64) xy_overlap=(0.8,0.8)', fontsize = 20)

axs[3].imshow(window_img_1)
axs[3].set_title('xy_window=(64,64) xy_overlap=(0.5,0.5)', fontsize = 20)

axs[4].imshow(window_img_4)
axs[4].set_title('xy_window=(32,32) xy_overlap=(0.5,0.5)', fontsize = 20)

axs[5].imshow(window_img_5)
axs[5].set_title('xy_window=(96,96) xy_overlap=(0.5,0.5)', fontsize = 20)

f.tight_layout()
plt.subplots_adjust(left=0.1, right=0.85, top=0.85, bottom=0.3)

2. Show some examples of test images to demonstrate how your pipeline is working. What did you do to optimize the performance of your classifier?

Ultimately I searched on multiple scales using HLS 3-channel HOG features plus spatially binned color and histograms of color in the feature vector, which provided a nice result. Those features extraction, classifying and window drawing were implemented in the function find_cars(). To enhance the efficiency of classifying, there are few code in find_cars() to extract HOG features just once for the entire region of interest in each full image / video frame. Please refer to the below images for your reference.

Import functions

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import random

% matplotlib inline

import pickle
from helper_functions import convert_color, find_cars

Read in test images

# Images are in JPEG format
test_images = glob.glob('./test_images/*.jpg')

# Creat a list to store the file paths
test_images_all = []
for image in test_images:
    test_images_all.append(image)

Define parameters

ystart = 400
ystop = 656
scale = 1.5

Run the following code once a new classifier is created

Save a dictonary for future use

# # Create dictionary
# dict_vehicle_detection = {"svc": svc, 
#                           "X_scaler": X_scaler_final, 
#                           "orient": orient_final, 
#                           "pix_per_cell": pix_per_cell_final, 
#                           "cell_per_block": cell_per_block_final, 
#                           "feature_vec": feature_vec_final, 
#                           "spatial_size": spatial_size_final, 
#                           "hog_channel": multichannel_final, 
#                           "hist_bins": hist_bins_final, 
#                           "hist_range": hist_range_final, 
#                           "spatial_feat": spatial_feat, 
#                           "hist_feat": hist_feat, 
#                           "hog_feat": hog_feat,
#                           "ystart": ystart,
#                           "ystop": ystop,
#                           "scale": scale
#                          }

# # Save dictionary
# pickle.dump(dict_vehicle_detection, open('dict_vehicle_detection.p', 'wb'))

Load data file

# Load dictionary
# dict_pickle = pickle.load(open('dict_vehicle_detection.p', 'rb'))
dict_pickle = pickle.load(open('HLS_ALL_HOG_9_8_2.p', 'rb'))

# Read data
svc = dict_pickle["svc"]
X_scaler = dict_pickle["X_scaler"]
orient = dict_pickle["orient"]
pix_per_cell = dict_pickle["pix_per_cell"]
cell_per_block = dict_pickle["cell_per_block"]
hog_channel = dict_pickle["hog_channel"]
spatial_size = dict_pickle["spatial_size"]
hist_bins = dict_pickle["hist_bins"]
hist_range = dict_pickle["hist_range"]
ystart = dict_pickle["ystart"]
ystop = dict_pickle["ystop"]
scale = dict_pickle["scale"]

Find cars in image

# The actual number of features in your final feature vector will be the total number of block positions multiplied by the number of cells per block, times the number of orientations, or in the case shown above: 
# number of block positions = window size (pixels) / pixels per cell - 1
# Example: 7 = 96/12 -1
# 7×7×2×2×9=1764.

# Initialize an empty list
list_out_img = []
list_windows = []

for img_path in test_images_all:
    # Read image
    img = mpimg.imread(img_path)    
    
    # Scan for window scale (96, 96) and cells per step 8
    window_size = 96
    cell_per_step = 4
    pix_per_cell = 12 # Change pixe per cell to make the total number of block positions = 8
    # Find cars in the image
    out_img_1, list_windows_1 = find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, hist_range, window_size, cell_per_step)
    # Scan for window scale (64, 64) and cells per step 4
    window_size = 64
    cell_per_step = 4
    pix_per_cell = 8 # Change pixe per cell to make the total number of block positions = 8
    # Find cars in the image
    out_img_2, list_windows_2 = find_cars(out_img_1, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, hist_range, window_size, cell_per_step)
    # Append the output image to the list 
    list_out_img.append(out_img_2)
    # Append the matched windows to the list 
#     print(len(list_windows_1))
#     print(len(list_windows_2))
    if 0 == len(list_windows_1):
        hstack = list_windows_2
    elif 0 == len(list_windows_2):
        hstack = list_windows_1
    else:
        hstack = np.vstack((list_windows_1, list_windows_2))
    list_windows.append(hstack)

Plot some example images

# Plot the examples
f, axs = plt.subplots(2, 3, figsize=(36, 24))
axs = axs.ravel()

for idx in range(len(list_out_img)):
    # Plot image
    axs[idx].imshow(list_out_img[idx])
    # Set title name
    title_name = test_images_all[idx].split('\\')[-1]
    axs[idx].set_title(title_name, fontsize = 20)

f.tight_layout()
plt.subplots_adjust(left=0.1, right=0.85, top=0.85, bottom=0.3)

Video Implementation

1. Provide a link to your final video output. Your pipeline should perform reasonably well on the entire project video (somewhat wobbly or unstable bounding boxes are ok as long as you are identifying the vehicles most of the time with minimal false positives.)

Here's a link to my video result

Import Libraries

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

#import numpy as np
from helper_functions import process_image

Process Video
Test on the given video project_video.mp4

video_output = 'test_videos_output/project_video_output.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
# clip1 = VideoFileClip("project_video.mp4").subclip(0,3)
clip1 = VideoFileClip("project_video.mp4")
video_output_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time video_output_clip.write_videofile(video_output, audio=False)

[MoviePy] >>>> Building video test_videos_output/project_video_output.mp4
[MoviePy] Writing video test_videos_output/project_video_output.mp4

100%|█████████▉| 1260/1261 [14:28<00:00,  1.49it/s]

[MoviePy] Done.
[MoviePy] >>>> Video ready: test_videos_output/project_video_output.mp4 

CPU times: user 16min 29s, sys: 5.14 s, total: 16min 34s
Wall time: 14min 29s

Play Video Inline

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(video_output))

2. Describe how (and identify where in your code) you implemented some kind of filter for false positives and some method for combining overlapping bounding boxes.

I recorded the multiple scales position of positive detection in each frame of the video. From the positive detections I created a heatmap and then thresholded that map to identify vehicle positions. I then used scipy.ndimage.measurements.label() to identify individual blobs in the heatmap. I then assumed each blob corresponded to a vehicle. I constructed bounding boxes to cover the area of each blob detected.

Below are six drawn test images, their corresponding threshold images and the final heatmap.

Import librarires

from scipy.ndimage.measurements import label

Import functions

from helper_functions import add_heat, apply_threshold, draw_labeled_bboxes

Run functions

# Initialize an empty list
list_draw_img = []
list_heatmap = []

for idx in range(len(test_images_all)):
    # Read image
    image = mpimg.imread(test_images_all[idx])
    heat = np.zeros_like(image[:,:,0]).astype(np.float)

    # Add heat to each box in box list
    heat = add_heat(heat,list_windows[idx])

    # Apply threshold to help remove false positives
    heat = apply_threshold(heat,2)

    # Visualize the heatmap when displaying    
    heatmap = np.clip(heat, 0, 255)

    # Find final boxes from heatmap using label function
    labels = label(heatmap)
    draw_img = draw_labeled_bboxes(np.copy(image), labels)
    
    # Append the output image to the list 
    list_draw_img.append(draw_img)
    
    # Append the output heatmap to the list 
    list_heatmap.append(heatmap)

Plot images

# Plot the draw images
f, axs = plt.subplots(6, 3, figsize=(36, 24))
axs = axs.ravel()

for idx in range(len(list_draw_img)):
    # Plot image
    axs[idx*3].imshow(list_out_img[idx])
    # Set title name
    # title_name = test_images_all[idx].split('\\')[-1]
    title_name = 'Car Positions'
    axs[idx*3].set_title(title_name, fontsize = 20)
    
    # Plot image
    axs[idx*3+1].imshow(list_draw_img[idx])
    # Set title name
    # title_name = test_images_all[idx].split('\\')[-1]
    title_name = 'Car Positions'
    axs[idx*3+1].set_title(title_name, fontsize = 20)

    # Plot image
    axs[idx*3+2].imshow(list_heatmap[idx], cmap='hot')
    # Set title name
    # title_name = test_images_all[idx].split('\\')[-1]
    title_name = 'Heatmap'
    axs[idx*3+2].set_title(title_name, fontsize = 20)

f.tight_layout()
plt.subplots_adjust(left=0.1, right=0.85, top=0.85, bottom=0.3)

Discussion

1. Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?

Here are my steps to approach to the final pipeline.

• Created my pipeline architecture with a small amout of test data

  • Extract the specific features from the dataset
  • Normalie the extracted features vector
  • Define labels vector
  • Split up and randomize on both extracted features vector and labels vector
    • Return training dataset and test dataset
  • Create SVM
  • Fit the training dataset into SVM
  • Evaluate the accuracy of SVM
  • Predict the result with the test dataset

• Tried different combination of color spaces and HOG features.

• Tried different heatmap threshold to filter out the false positive

Here are my problems/issues

1. Length of feature vector doesn't match the scaler
   • Fixed by controling the dimension of the feature vector
2. Too many or too less matched windows
   • Improved by using moderate window size and overlap
   • Improved by tunning the color spaces
   • Improved by tunning the HOG feature parameters
3. Drawing window in the shawdow of the tree.
   • Record the previous position and predict the next window appearance.
   • Calculate the width of window, discard the window if it is too wide.